How Axis Community Health Improved Patient Care by Tracking Behavioral Health Data and Quality Outcomes

Sep 11, 2020 • Focus Area: Population Health • Program: Population Health Learning Network

Axis Community Health is a community health center located in the Tri-Valley area in Northern California, encompassing Dublin, Livermore, Pleasanton, and San Ramon. Axis offers medical care, behavioral health services, and dental services to people of all ages as part of its mission to provide affordable, accessible, and compassionate health care. It operates out of five sites, employs 165 staff members, and serves more than 14,000 patients each year.

Through participation in the Population Health Learning Network, Axis implemented a project to improve its behavioral health data infrastructure to better track patient outcomes, adjust treatment, and provide quality care.

The following case study was prepared by John Snow, Inc.

Population Health Learning Network

CCI’s goal for the Population Health Learning Network was to improve the health and well-being of more than 750,000 Californians by bringing together safety net primary care organizations to strengthen and advance their population health management strategies. In 2018, Axis received funding, coaching, and technical assistance to implement strategies for population health management and work towards a chosen goal.

Population Health Project: Behavioral Health Tracking

Challenge

Axis serves hundreds of patients seeking behavioral health care each week. However, the behavioral health department previously lacked the systematized and comprehensive access to data that other departments, such as primary care, have created and utilized. Despite using various tools to document behavioral health outcomes, tracking and monitoring patients’ depression, anxiety, and drug use over time had been difficult. The department’s main barrier was a lack of technology infrastructure to pull data and analyze it longitudinally – that is, to track trends in patient care and outcomes over time. Data is particularly important in population health management to identify which specific populations are receiving care, measure the outcomes, and adjust care accordingly. The inability to track patients often prolonged patients’ time in care and made it difficult for the team to evaluate treatment methods.

“We lacked organization around the severity of our patient health, specifically for our patients who have depression. We didn’t have a means of tracking improvements over time and throughout treatment. Our therapists did not have automated or real-time reports on their panels, which made it difficult for them to review and act on in a timely manner.” – Afsheen Islam, Quality Enhancement Manager

Goals

Create a pilot study registry to track scores throughout treatment from the Patient Health Questionnaire-9 (PHQ-9) — a brief tool that screens for and measures the severity of a patient’s depression — and follow up by expanding the registry to include scores from screening tools that measure anxiety and drug use.
Examine data through the registry to identify patients whose behavioral health scores were not improving.
Adjust treatment as needed.
Measure improvements.

Team

Axis’ PHLN project team was led by the organization’s Quality Enhancement Manager and the Chief Quality and Transformation Officer, together with the Chief of Behavioral Health, who has encouraged and facilitated the creation of a data registry. In order to fulfill the goals of the project, they collaborated with members of the behavioral health team, which includes 19 licensed providers that see 500 to 600 patients a week, as well as a consulting psychiatrist and managing providers who help with medication dissemination.

Key Changes

Axis created a system to engage behavioral health providers in using data to drive and improve outcomes among its patient population with behavioral health diagnoses. Through the PHLN, Axis developed a robust behavioral health registry within Epic that monitors and tracks key health indicators. Axis specifically tracks patients’ scores from the PHQ-9 (which screens for depression severity), Generalized Anxiety Disorder-7, or GAD-7 (which screens for generalized anxiety disorder and its severity), and the Drug Abuse Screening Tool, or DAST-10 (which screens for drug use and provides an index of the degree and types of problems that drug use has caused the patient). The registry equips providers to use this data to adjust treatment plans in a timely manner.

Strategy

1. Track Patient Scores

Patients are asked to complete the PHQ-9, GAD-7, and DAST screenings on a tablet when they arrive at the health center. When submitted, the data is automatically integrated into Epic. Using SQL, the standard query language used to communicate with a database, the team created a query that pulls key information into a behavioral health patient registry. The registry is comprised of the following information, which was initially tracked over a five-month period:

Patient name
Date of appointment
Provider seen
Current medications
Screening Scores (initially just PHQ-9 in the pilot)
Provider who entered screening scores
ICD-10 diagnosis code

Figure 1: Example of Axis Data Registry Query

2. Examine Data in the Registry

Axis analyzed patient data in the registry based on the above fields, which was beneficial since this data had not previously been available in one central location. It was particularly useful to track patients’ current medications to see which medications had been tapered on and off. With the project, Axis’ behavioral health registry report was generated automatically every month, reducing unnecessary manual repetition in sending it out. The Chief of Behavioral Health then printed the report for everyone to review as a group during monthly team meetings, rather than individually. In addition, if therapists chose to share data, patients could receive reports during appointments with behavioral health data such as their PHQ-9 trends, which empowered them to track their progress over time.

Figure 2: Example of Patient Report with Registry Data

“Data is being shared at the board level, at the senior leadership level, all the way down to the patient themselves, and that’s really cool to see.” – Amit Pabla, Chief Quality and Transformation Officer

3. Adjust Treatment

The behavioral health team had access to data from the registry that allowed them to adjust treatment based on individual changes in screening scores over time. The ICD-10 diagnosis codes tracked in the registry were used to determine if patients were eligible for additional treatment. The behavioral health team looked at data for patients who (according to their screening scores) were not improving, and referred them to psychiatrists and adjusted medication when necessary. Team members also moved patients out of treatment when they were ready to be discharged, thus freeing up space for other patients in need.

4. Measure Improvement

Axis used data from the registry to measure improvement in patients’ behavioral health screening scores based on duration of treatment and/or changes in medication. In addition to tracking individual behavioral health outcomes, Axis used the registry to evaluate its behavioral health program by looking at outcomes on a macro level. Specifically, they analyzed data by each provider across the whole behavioral health team. Results were sent to the department directors and the Chief of Behavioral Health for overall tracking. The data was also sent to behavioral health providers and the consulting psychiatrist to review specific cases during case consultation. They also monitored behavioral health provider performance based upon the data to determine where additional training might be needed.

Project Results/Impact

Axis’ data registry has allowed its behavioral health team to analyze patients’ data and understand their progress or lack thereof. The analysis that Axis completed has led to changes in treatment protocols that have improved behavioral health outcomes (see below). In addition, Axis reported increased and sustained data sharing between providers.

From April 1^st, 2019 to September 30^th, 2019, Axis tracked 207 patients who were cared for by 15 behavioral health providers. One hundred and fifteen of those patients only came in once, and therefore only had one PHQ-9 score to report. However, for the remaining 92 patients, Axis reported that 59 patients (64%) had PHQ-9 scores that improved over time (Figure 3). Specifically, 16 improved by 50% or more, while 43 improved by less than 50%. Axis also reported that 26 patients had PHQ-9 scores that worsened over time, and seven patients whose scores stayed constant. In addition, behavioral health providers anecdotally noticed that patients were increasingly open to adjusting their treatment plans after reviewing personal data.

Figure 3. Distribution of PHQ-9 Score Trends Among Axis Patients With Repeat Visits

Axis’s behavioral health team also used the registry to determine when to discharge patients based on consistent improvement in their behavioral health outcomes. This enabled them to bring in other patients who needed therapist services more urgently.

Adapting to COVID-19

Axis was grateful to have the data registry up and running as the COVID-19 pandemic gained speed. Staff acted quickly to pull a report of patients who were at high risk for COVID-19 – a report that included information such as PHQ-9 scores, A1C levels, blood pressure readings, and others that suggested a high risk of depression, diabetes, and/or hypertension. Nurses contacted these patients early on in the pandemic, informing them of options to engage in telehealth. This population health approach meant Axis’ high-risk patients knew about services available to them to receive care, rather than being left to reach out to their providers on their own. In a time of heightened confusion and anxiety, having proactive patient outreach emphasized the benefit of a data-driven, population health management approach to preventive care.

Barriers

Axis PHLN team identified three significant hurdles when implementing its population health management strategy.

Data Learning Curve

Axis’ PHLN Team had to learn to navigate new tools and build out reports, both of which are time-intensive. The PHLN Team also had to make sure that data was accurate and that the behavioral health team could understand and use it to improve care. Axis’ PHLN team reported that when the data was not in an accessible format for providers, the registry was underutilized. They were able to overcome this barrier by developing various prototypes, eliciting feedback from their team, and iterating, to make the registry as impactful as possible for providers.

Timeliness

The PHLN team sought to build the registry and track, monitor, and evaluate its initial data all within the second year of its PHLN project. Balancing high quality, accessible behavioral health data with rapid reporting was challenging for the population health team. Once the initial reporting structure was created, behavioral health providers had the ability to pull data from the registry on demand if they chose to, although they usually reviewed the data comprehensively during their monthly meetings.

Provider Access and Coordination

Axis’ data highlighted a large demand for psychiatrists to meet the behavioral health needs of the health center’s patients. While Axis has 19 behavioral health providers in-house, its consulting psychiatrist has to split time between eight community health centers. Thankfully, due to the existence of the registry, the consulting psychiatrist is now able to analyze organized data and discuss it with behavioral health providers, which has increased his efficacy in his limited time with Axis.

Next Steps to Spread Impact

Axis’ PHLN team is working to implement additional data metrics to the registry in order to capture more nuanced and comprehensive patient data in behavioral health and primary care. Further, the team is working to increase communication and data sharing between providers.

Axis’ behavioral health team has begun using EMDR (Eye Movement Desensitization Reprocessing) as a form of psychotherapy, and the PHLN team is planning to include this as a column in their reports to see if it is positively correlated with patients’ PHQ-9 scores.
The PHLN team will continue adding data around other treatment methods, other disease areas such as diabetes and hypertension, and metrics such as patient satisfaction scores.
Axis also hopes to utilize communication channels within Epic so that the consulting psychiatrist, therapists, and primary care providers can communicate easily. This is important to centralize administrative and clinical information in one place and coordinate patient care.

“I always think that dashboards and reporting is a work in progress. We always want more data.” – Amit Pabla, Chief Quality and Transformation Officer

Lessons Learned

Axis’ experience revealed four lessons with implications for other community health centers that seek to improve data integrity, behavioral health outcomes, and overall population health capacity.

Invest in technology infrastructure. Axis did not shy away from the challenges and workload associated with implementing new data infrastructure. In addition to learning the Epic reporting system, team members took it upon themselves to learn SQL to ensure that their reports worked exactly as they imagined and enable more flexibility. Now that the systems are in place, Axis has the flexibility to create relevant data visualizations rather than relying on a system that did not quite meet its needs.
Narrow down your pilot patient population. Initially, Axis focused only on patients who had a PHQ-9 score of 15 or above, which signifies moderately severe or severe depression. Since that one criteria alone yielded a large group of patients, the team chose to narrow down the patient population for the pilot. Therefore, Axis identified other criteria, in addition to PHQ-9 score, which helped team members focus on a smaller subset and measure change effectively. The additional criteria included patients who had at least one behavioral health visit in the past six months and patients who had a qualifying depression diagnosis code. Once the pilot was successful, they expanded to include other patient groups, such as people with anxiety and substance use disorders.
Conduct team-based research. It was important for the team that created the report to understand how behavioral health worked and what the data meant so that it could generate reports that would actually be utilized. Axis’ team had their consulting psychiatrist weigh in on the format early on, so that he was excited to use it once complete.
Engage champions. The Chief of Behavioral Health was an essential data advocate from the beginning. She worked with the team to ensure that their data was accurate and accessible for providers across the behavioral health department, thus increasing buy-in and highlighting the necessity of the registry from a leadership perspective. Specifically, she worked with the team to pull data straight from Epic based upon patients’ monthly completion of screening questionnaires throughout treatment. The behavioral health team reviewed the data over time and provided additional background information about why a patient’s score might not have been improving. Additionally, providers also sometimes shared the data with the patients directly so they could view the trends as well.

“This project has helped us get our foot in the door and open up from there. There’s so much potential now in terms of what we can do. This was our first project using Epic. The PHLN team created a new landscape, so sky’s the limit now!” – Amit Pabla, Chief Quality and Transformation Officer